Search Results (5)

To address the limited data availability and low predictive accuracy of nickel matte grade models in the early stages of facility operation, this study introduces a unique stepwise prediction methodology that integrates data augmentation and ensemble learning, specifically tailored for limited industrial datasets. Predicting matte nickel grade accurately is critical for nickel sulfate production, a key precursor in cathode manufacturing. However, in newly adopted facilities, operational data are scarce, posing a major challenge for conventional machine learning models that require large, well-balanced datasets to generalize effectively. Moreover, the nonlinear dependencies between raw material composition, operational conditions, and metallurgical reactions further complicate the prediction task, often leading to high errors in traditional regression models. To overcome these challenges, this study introduces an innovative approach that integrates feature engineering, Gaussian noise augmentation, SMOTE regression, and a stacking ensemble model, using XGBoost (2.0.3) and CatBoost (1.2.7). First, input variables were refined through feature engineering, followed by data augmentation to enhance dataset diversity and improve model robustness. Next, a stacking ensemble framework was implemented to mitigate overfitting and enhance predictive accuracy. Finally, SHAP, an XAI technique that quantifies the impact of each input variable on the model’s predictions based on cooperative game theory, was employed to interpret key process variables, offering deeper insights into the factors influencing nickel grade. The experimental results demonstrate a substantial improvement in prediction accuracy, with the R² coefficient increasing from 0.3050 to 0.9245, alongside significant reductions in RMSE, MAE, and MAPE. The proposed methodology not only enhances predictive performance in data-scarce industrial environments but also provides an interpretable framework for real-world process optimization. These findings validate its applicability to nickel matte operations, offering a scalable and explainable machine learning approach for metallurgical industries with limited data availability. Full article

Addressing the issues of low prediction accuracy and poor interpretability in traditional matte grade prediction models, which rely on pre-smelting input and assay data for regression, we incorporate process sensors’ data and propose a temporal network based on Time to Vector (Time2Vec) and temporal convolutional network combined with temporal multi-head attention (TCN-TMHA) to tackle the weak temporal characteristics and uncertain periodic information in the copper smelting process. Firstly, we employed the maximum information coefficient (MIC) criterion to select temporal process sensors’ data strongly correlated with matte grade. Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. Finally, we implemented the TCN-TMHA module and used specific weighting mechanisms to assign weights to the input features and prioritize relevant key time step features. Experimental results indicate that the proposed model yields more accurate predictions of copper content, and the coefficient of determination (R²) is improved by 2.13% to 11.95% and reduced compared to the existing matte grade prediction models. Full article

Due to the scarcity of modeling samples and the low prediction accuracy of the matte grade prediction model in the copper melting process, a new prediction method is proposed. This method is based on enhanced generative adversarial networks (EGANs) and random forests (RFs). Firstly, the maximum relevance minimum redundancy (MRMR) algorithm is utilized to screen the key influencing factors of matte grade and remove redundant information. Secondly, the GAN data augmentation model containing different activation functions is constructed. And, the generated data fusion criterion based on the root mean squared error (RMSE) and the coefficient of determination (R²) is designed, which can tap into the global character distributions of the copper melting data to improve the quality of the generated data. Finally, a matte grade prediction model based on RF is constructed, and the industrial data collected from the copper smelting process are used to verify the effectiveness of the model. The experimental results show that the proposed method can obtain high-quality generated data, and the prediction accuracy is better than other models. The R² is improved by at least 2.68%, and other indicators such as RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE) are significantly improved. Full article

The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model. Full article

Mineable platinum group metal (PGM) deposits are rare and found in relatively few areas of the world. At the same time, the use of PGM is predicted to expand in green technology and energy applications, and PGMs are consequently currently listed as European Union critical metals. Increased mineralogical complexity, lower grade ores, and recent PGM production expansions give rise to the evaluation of the value chain of the capital-intensive conventional matte smelting treatment and other processing possibilities of the ore. This article will review the processes and value chain developed to treat ores for PGM recovery, highlighting hydrometallurgical refining approaches. It groups processes according to their rationale and discusses the special features of each group. Full article